Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A method for dynamic inspection and filtering in a containerized environment, comprising: monitoring the containerized environment to identify deployment of a software container in the containerized environment; inspecting traffic redirected from the software container, wherein the inspecting includes detecting malicious activity of the software container; and filtering the traffic based on at least one filtering rule when the malicious activity is detected, wherein the at least one filtering rule is defined in a filtering profile for the software container, wherein the filtering profile is determined for the software container when a new container image of the software container is detected in the containerized environment.
This invention relates to dynamic inspection and filtering of traffic in containerized environments to detect and mitigate malicious activity. Containerized environments, which use isolated software containers to deploy applications, face challenges in securing traffic between containers and external systems. Existing solutions often lack real-time inspection and adaptive filtering capabilities, leaving vulnerabilities unaddressed. The method involves monitoring a containerized environment to detect the deployment of a software container. Once deployed, traffic redirected from the container is inspected for signs of malicious activity, such as unauthorized access, data exfiltration, or anomalous behavior. If malicious activity is detected, the traffic is filtered based on predefined rules stored in a filtering profile. This profile is dynamically generated or updated when a new container image is detected, ensuring that the filtering rules remain relevant to the specific software version in use. The filtering rules may include blocking certain traffic patterns, restricting network access, or enforcing encryption requirements. By dynamically adapting to new container deployments, the system provides continuous security without manual intervention. This approach enhances security in containerized environments by combining real-time monitoring with adaptive filtering.
2. The method of claim 1 , further comprising: analyzing contents of the new container image to determine a type of application to be executed by the software container; and determining, based on the type of application, the filtering profile for the software container.
A method for managing software containers in a computing environment involves analyzing the contents of a new container image to determine the type of application it will execute. The method then selects a filtering profile for the container based on the identified application type. This filtering profile defines rules for monitoring and controlling the container's behavior, such as network traffic, system calls, or resource usage, to enhance security and performance. The approach ensures that containers execute with appropriate restrictions tailored to their specific application type, reducing risks like unauthorized access or resource misuse. The method may also include deploying the container with the selected filtering profile, allowing dynamic adaptation to different workloads. This technique is particularly useful in cloud computing and microservices architectures where containers run diverse applications with varying security and operational requirements. By automating the selection of filtering profiles, the method improves efficiency and consistency in container management.
3. The method of claim 2 , further comprising: extracting the contents of the new container image, wherein extracting the contents of the new container image includes extracting contents of each layer of the new container image.
This invention relates to container image analysis, specifically a method for extracting and examining the contents of container images used in software deployment. The problem addressed is the need to thoroughly inspect container images, which are often composed of multiple layers, to ensure security, compliance, or compatibility before deployment. Existing methods may not fully analyze all layers of a container image, leaving potential vulnerabilities or inconsistencies undetected. The method involves extracting the contents of a new container image, with a focus on extracting contents from each individual layer of the container image. This layered extraction allows for detailed inspection of each component within the container, including dependencies, configurations, and potential security risks. By analyzing each layer separately, the method ensures comprehensive visibility into the container's structure and contents, enabling better detection of issues such as outdated libraries, unauthorized software, or misconfigurations. The extracted contents can then be used for further security scanning, compliance checks, or optimization processes. This approach improves the reliability and security of containerized applications by ensuring that all layers of the container image are thoroughly examined.
4. The method of claim 2 , further comprising: creating, based on the analysis of the contents of the new container image, a runtime model for the new container image, wherein the runtime model defines expected runtime behavior for the software container executing the new container image, wherein the malicious activity is detected based further on the runtime model.
This invention relates to container security, specifically detecting malicious activity in software containers by analyzing container images and their runtime behavior. The problem addressed is the challenge of identifying malicious or anomalous behavior in containerized applications, which can be difficult to detect using traditional static analysis alone. The method involves analyzing the contents of a new container image to identify potential security risks, such as vulnerabilities or suspicious artifacts. This analysis includes examining the image's layers, dependencies, and configurations to detect known threats or deviations from expected behavior. Additionally, a runtime model is generated for the new container image, which defines the expected runtime behavior of the software container executing the image. This model serves as a baseline for monitoring the container's behavior during execution. The runtime model is used to detect malicious activity by comparing the container's actual behavior against the expected behavior defined in the model. Any deviations or anomalies that indicate potential security threats are flagged for further investigation. This approach enhances security by combining static analysis of container images with dynamic monitoring of runtime behavior, providing a more comprehensive defense against malicious activities.
5. The method of claim 2 , further comprising: generating, based on the analysis of the contents of the new container image, a routing rule when the deployment of the software container is identified, wherein the routing rule is for redirecting the traffic when the traffic is directed to the software container.
This invention relates to containerized software deployment and traffic management in computing environments. The problem addressed is the need to dynamically route traffic to newly deployed software containers based on their contents, ensuring efficient and secure traffic distribution. The method involves analyzing the contents of a new container image to determine its deployment characteristics. This analysis includes examining the container's configuration, dependencies, and other metadata to identify how the software container should be deployed. Once the deployment is identified, a routing rule is generated based on the analysis. This routing rule is used to redirect traffic to the software container when requests are directed to it. The routing rule ensures that traffic is properly managed and directed to the correct container, improving system efficiency and security. The method may also include deploying the software container in a computing environment, such as a cloud or on-premises infrastructure, and monitoring the container's performance to adjust routing rules as needed. The analysis of the container image may involve parsing configuration files, inspecting dependencies, or using machine learning models to predict deployment requirements. The routing rule can be implemented using network policies, load balancers, or other traffic management systems to ensure optimal traffic distribution. This approach automates the process of traffic routing, reducing manual configuration and improving system scalability.
6. The method of claim 2 , further comprising: determining, based on the analysis of the contents of the new container image, an application type of the new container image, wherein the malicious activity is detected based further on at least one predetermined attack signature associated with the application type.
This invention relates to container security, specifically detecting malicious activity in containerized applications. The problem addressed is the challenge of identifying and mitigating threats in container images, which are increasingly targeted by attackers due to their dynamic and ephemeral nature. Traditional security tools often fail to detect application-specific threats in container environments. The method involves analyzing the contents of a new container image to determine its application type. This analysis includes examining the image's layers, dependencies, and configurations to classify the application (e.g., web server, database, or microservice). Once the application type is identified, the system cross-references the image's contents against predetermined attack signatures specific to that application type. These signatures represent known vulnerabilities or attack patterns for the identified application. By combining this application-specific analysis with broader threat detection techniques, the system improves the accuracy of identifying malicious activity in container images. This approach ensures that security measures are tailored to the specific risks associated with the application running in the container, reducing false positives and enhancing threat detection.
7. The method of claim 1 , wherein the traffic is encrypted traffic, wherein inspecting the traffic further comprises: interfacing with the software container to retrieve at least one key from the software container; and decrypting the traffic using the retrieved at least one key.
This invention relates to network traffic inspection, specifically for encrypted traffic within software container environments. The problem addressed is the challenge of inspecting encrypted traffic in containerized applications, where traditional inspection methods cannot access encrypted data due to lack of decryption keys. The method involves inspecting encrypted traffic by interfacing directly with the software container to retrieve decryption keys. Once the keys are obtained, the encrypted traffic is decrypted using these keys, enabling inspection of the decrypted data. This approach ensures that encrypted traffic within containerized environments can be analyzed for security, compliance, or performance monitoring purposes. The software container provides a secure and isolated environment for running applications, and the method leverages this environment to access the necessary decryption keys. By retrieving keys from the container, the system bypasses the limitations of traditional inspection tools that cannot decrypt traffic without direct access to the keys. This allows for comprehensive traffic analysis while maintaining the security and isolation benefits of containerization. The method is particularly useful in cloud-native and microservices architectures, where encrypted traffic between containerized services is common. It enables security teams to monitor and inspect traffic without compromising the integrity of the containerized applications. The solution ensures that encrypted traffic can be inspected without requiring modifications to the applications or the container runtime, making it a non-intrusive and scalable approach.
8. The method of claim 1 , wherein the malicious activity is detected when an abnormality in execution of the software container is detected, wherein the abnormality is detected as a deviation from learned behavior of the software container.
This invention relates to detecting malicious activity in software containers by monitoring deviations from learned behavior. Software containers are widely used to package and deploy applications, but they can be vulnerable to attacks that exploit abnormal execution patterns. The invention addresses this by establishing a baseline of normal behavior for a software container and then identifying anomalies that may indicate malicious activity. The method involves continuously monitoring the execution of a software container to detect deviations from its expected behavior. This includes tracking various execution parameters such as resource usage, process activity, network traffic, and system calls. Machine learning or statistical models are used to learn the normal behavior of the container over time. When the system detects a significant deviation from this learned baseline, it flags the container as potentially compromised. The invention also includes mechanisms to adapt the learned behavior model as the container evolves, ensuring that legitimate changes in behavior are not mistakenly flagged as malicious. If an anomaly is detected, the system can trigger automated responses such as isolating the container, alerting administrators, or terminating the container to prevent further damage. This approach improves security by proactively identifying and mitigating threats before they escalate.
9. The method of claim 8 , wherein the learned behavior includes at least one of: hypertext transfer protocol (HTTP) verbs, application programming interface (API) routes, and query parameters.
This invention relates to systems for analyzing and learning behavioral patterns in networked applications, particularly those involving web-based interactions. The technology addresses the challenge of dynamically identifying and adapting to the structure and behavior of web services, such as those using HTTP-based APIs. The method involves training a model to recognize and predict patterns in how applications interact with web services, including the specific HTTP methods (verbs) used, the API routes accessed, and the query parameters passed during requests. By learning these behavioral elements, the system can automate or optimize interactions with web services, reducing manual configuration and improving efficiency. The learned behaviors may be applied to tasks such as API testing, automation, or security analysis, where understanding the expected structure of requests is critical. The invention enhances the ability of systems to dynamically adapt to changes in web service behavior without requiring explicit programming or manual updates. This approach is particularly useful in environments where web services frequently evolve, such as cloud-based or microservices architectures. The method ensures that the system remains aligned with the latest API specifications and usage patterns, improving reliability and reducing errors in automated interactions.
10. The method of claim 8 , wherein the learned behavior includes a pattern of requests and corresponding responses.
A system and method for analyzing and learning behavioral patterns in networked environments, particularly for improving request-response interactions in distributed systems. The technology addresses inefficiencies in handling repeated or predictable request-response sequences, which can lead to redundant processing, latency, or resource waste. The invention involves a learning mechanism that identifies and stores patterns of requests and their corresponding responses, enabling optimized handling of future interactions. By recognizing these patterns, the system can preemptively prepare responses, cache results, or route requests more efficiently, reducing computational overhead and improving system performance. The learned behavior includes not only the sequence of requests but also the contextual details of responses, allowing for adaptive and context-aware optimizations. This approach is applicable in various domains, such as web services, database systems, or IoT networks, where repetitive or predictable interactions are common. The method enhances scalability and responsiveness by minimizing redundant operations and leveraging historical data to anticipate and streamline future interactions.
11. The method of claim 8 , wherein the learned behavior includes user interactions with the software container.
A system and method for analyzing and learning user interactions with software containers to improve software deployment and management. The technology addresses the challenge of optimizing software container behavior based on user engagement patterns, ensuring efficient resource utilization and enhanced user experience. The method involves monitoring and recording user interactions with software containers, such as application launches, configuration changes, and resource usage patterns. These interactions are processed to identify behavioral trends, which are then used to adjust container settings, allocate resources dynamically, and predict future user needs. By learning from these interactions, the system can automate container management tasks, reduce manual intervention, and improve overall system performance. The learned behavior may include specific user actions, such as how often a user accesses certain features, the frequency of container restarts, or the types of configurations applied. This data-driven approach ensures that software containers operate in a manner aligned with user preferences and system requirements, leading to more efficient and responsive software deployments. The system may also integrate with existing container orchestration tools to apply these learned behaviors at scale, optimizing large-scale deployments in cloud or on-premises environments.
12. A non-transitory computer readable medium having stored thereon instructions for causing a processing circuitry to execute a process for dynamic inspection and filtering in a containerized environment, the process comprising: monitoring the containerized environment to identify deployment of a software container in the containerized environment; inspecting traffic redirected from the software container, wherein the inspecting includes detecting malicious activity of the software container; and filtering the traffic based on at least one filtering rule when the malicious activity is detected, wherein the at least one filtering rule is defined in a filtering profile for the software container, wherein the filtering profile is determined for the software container when a new container image of the software container is detected in the containerized environment.
This invention relates to dynamic inspection and filtering of traffic in containerized environments to detect and mitigate malicious activity. In modern computing, containerized environments are widely used to deploy and run software applications in isolated, lightweight virtualized environments. However, these environments can be vulnerable to security threats, such as malicious containers executing harmful activities. The invention addresses this problem by providing a system that monitors the containerized environment to detect the deployment of new software containers. When a container is deployed, traffic from the container is inspected for signs of malicious activity, such as unauthorized access, data exfiltration, or other suspicious behavior. If malicious activity is detected, the system applies predefined filtering rules to block or modify the traffic, preventing potential damage. The filtering rules are stored in a filtering profile, which is dynamically generated or updated when a new container image is detected in the environment. This ensures that the system adapts to changes in the containerized environment, maintaining security without requiring manual intervention. The solution enhances security in containerized environments by automating the detection and mitigation of threats, reducing the risk of attacks and improving overall system resilience.
13. A system for dynamic inspection and filtering, wherein the system hosts a containerized environment, comprising: a processing circuitry; and a memory, the memory containing instructions that, when executed by the processing circuitry, configure the system to: monitor the containerized environment to identify deployment of a software container in the containerized environment; inspect traffic redirected from the software container to the system, wherein the inspecting includes detecting malicious activity of the software container; and filter the traffic based on at least one filtering rule when the malicious activity is detected, wherein the at least one filtering rule is defined in a filtering profile for the software container, wherein the filtering profile is determined for the software container when a new container image of the software container is detected in the containerized environment.
This invention relates to dynamic inspection and filtering of containerized environments to detect and mitigate malicious activity. In modern computing, containerized environments host multiple software containers, which can introduce security risks if not properly monitored. The system addresses this by dynamically inspecting and filtering traffic from containers to prevent malicious behavior. The system includes processing circuitry and memory storing instructions to monitor a containerized environment for the deployment of software containers. When a new container is detected, the system inspects traffic redirected from the container to identify malicious activity, such as unauthorized access or data exfiltration. If malicious activity is detected, the system applies filtering rules from a predefined filtering profile to block or restrict the traffic. The filtering profile is dynamically determined when a new container image is detected, ensuring that security policies are tailored to the specific container. The system operates within the containerized environment, allowing real-time inspection and filtering without requiring external intervention. By dynamically adjusting filtering rules based on container behavior, the system enhances security while maintaining operational efficiency. This approach reduces the risk of malicious containers compromising the environment.
14. The system of claim 13 , wherein the system is further configured to: analyze contents of the new container image to determine a type of application to be executed by the software container; and determine, based on the type of application, the filtering profile for the software container.
This invention relates to containerized application security, specifically a system for dynamically applying filtering profiles to software containers based on the type of application they execute. The system addresses the challenge of securing containerized applications by automatically determining and enforcing appropriate security policies without manual intervention. The system operates by analyzing the contents of a new container image to identify the type of application it will run. This analysis may involve inspecting metadata, dependencies, or other characteristics of the image. Based on the identified application type, the system selects a predefined filtering profile that includes security rules tailored to that application. For example, a web server container might receive a profile that restricts outbound network access to specific ports, while a data processing container might enforce stricter file system access controls. The filtering profile is then applied to the software container, enforcing the specified security policies during runtime. This approach ensures that containers execute with the minimum necessary permissions, reducing the attack surface and mitigating risks associated with misconfigured or overly permissive containers. The system may also support dynamic updates to filtering profiles if the application type or security requirements change. This solution improves container security by automating policy enforcement and adapting to different application workloads.
15. The system of claim 14 , wherein the system is further configured to: extract the contents of the new container image, wherein extracting the contents of the new container image includes extracting contents of each layer of the new container image.
This invention relates to container image analysis systems, specifically for extracting and examining the contents of container images used in software deployment. The system addresses the challenge of efficiently analyzing container images, which are often composed of multiple layered files, to identify vulnerabilities, compliance issues, or other security risks before deployment. The system is designed to process a new container image by first extracting its contents, including the contents of each individual layer within the image. Container images are built in layers, where each layer represents a modification or addition to the previous layer, and extracting all layers allows for a comprehensive analysis of the entire image structure. This extraction process enables subsequent security scanning, dependency analysis, or compliance checks to be performed on the extracted contents, ensuring that the container image meets required standards before deployment. By analyzing each layer, the system can detect issues such as outdated software versions, known vulnerabilities, or unauthorized components that may be present in any part of the image. This layered approach ensures that no part of the container image is overlooked, providing a thorough assessment of its security and integrity. The extracted contents can then be used for further processing, such as generating reports or applying remediation actions to address identified risks.
16. The system of claim 14 , wherein the system is further configured to: create, based on the analysis of the contents of the new container image, a runtime model for the new container image, wherein the runtime model defines expected runtime behavior for the software container executing the new container image, wherein the malicious activity is detected based further on the runtime model.
This invention relates to container security, specifically detecting malicious activity in software containers by analyzing container images. The system addresses the challenge of identifying threats in containerized applications, where traditional security tools often fail due to the dynamic and isolated nature of containers. The system analyzes the contents of a new container image to detect potential malicious activity. This includes examining the image's components, such as binaries, libraries, and configurations, to identify known vulnerabilities or suspicious patterns. The system then generates a runtime model for the container image, which defines the expected behavior of the software container when executing the image. This model includes normal operational parameters, such as network connections, file access, and process execution, to establish a baseline for legitimate activity. During runtime, the system monitors the container's behavior and compares it against the predefined runtime model. Deviations from the expected behavior, such as unauthorized network access or unexpected process execution, are flagged as potential malicious activity. The runtime model enhances detection accuracy by providing context-specific thresholds and patterns, reducing false positives and improving threat identification. This approach ensures that containers operate securely by enforcing behavioral constraints derived from the image analysis.
17. The system of claim 14 , wherein the system is further configured to: generate, based on the analysis of the contents of the new container image, a routing rule when the deployment of the software container is identified, wherein the routing rule is for redirecting the traffic when the traffic is directed to the software container.
A system for managing software container deployments in a computing environment analyzes the contents of a new container image to determine whether the deployment of a software container is required. When deployment is identified, the system generates a routing rule that redirects network traffic to the software container. The routing rule ensures that incoming traffic is properly directed to the deployed container, enabling seamless integration into the existing network infrastructure. This functionality automates the process of traffic management, reducing manual configuration and improving deployment efficiency. The system may also include components for monitoring container health, scaling deployments, and enforcing security policies, ensuring reliable and secure operation of the containerized applications. By dynamically generating routing rules based on container image analysis, the system simplifies deployment workflows and enhances operational flexibility in cloud-native and microservices architectures.
18. The system of claim 14 , wherein the system is further configured to: determine, based on the analysis of the contents of the new container image, an application type of the new container image, wherein the malicious activity is detected based further on at least one predetermined attack signature associated with the application type.
This system analyzes container images to detect malicious activity by examining their contents. The system identifies the application type of a new container image and uses this information to detect malicious behavior by comparing the image's contents against predetermined attack signatures specific to that application type. This approach enhances security by tailoring threat detection to the specific vulnerabilities and attack patterns associated with different applications running in containerized environments. The system leverages the application type to improve the accuracy and relevance of threat detection, reducing false positives and ensuring that security measures are aligned with the actual risks posed by the containerized application. By integrating application-specific attack signatures, the system provides a more targeted and effective defense against container-based threats, addressing the challenge of securing diverse applications running in dynamic container environments. The system's ability to dynamically adapt its detection mechanisms based on the application type ensures robust protection against evolving threats.
19. The system of claim 13 , wherein the traffic is encrypted traffic, wherein the system is further configured to: interface with the software container to retrieve at least one key from the software container; and decrypt the traffic using the retrieved at least one key.
This invention relates to a system for handling encrypted network traffic within a software container environment. The problem addressed is the secure decryption and processing of encrypted traffic within isolated containerized applications, ensuring data integrity and confidentiality while maintaining operational efficiency. The system interfaces with a software container to retrieve at least one cryptographic key stored within the container. Using this key, the system decrypts the encrypted traffic, enabling further processing or analysis. The container acts as a secure environment for key management, isolating cryptographic operations from the broader system to enhance security. The system may also include components for traffic interception, decryption, and subsequent handling, such as forwarding or logging the decrypted data. The containerized approach ensures that keys and decryption processes are contained within a controlled environment, reducing exposure to potential security threats. This method improves the security and manageability of encrypted traffic in distributed or cloud-based systems.
20. The system of claim 13 , wherein the malicious activity is detected when at least one abnormality in execution of the software container is detected.
A system for detecting malicious activity in software containers monitors the execution of containers to identify abnormalities indicative of security threats. The system analyzes container behavior, including resource usage, process execution, and network activity, to detect deviations from expected or baseline patterns. When an abnormality is detected, such as unusual CPU spikes, unauthorized process launches, or unexpected network connections, the system flags the container as potentially compromised. The detection mechanism may involve real-time monitoring, statistical analysis, or machine learning models trained on normal container behavior. The system can then trigger automated responses, such as isolating the container, terminating it, or alerting security personnel. This approach helps prevent malware, unauthorized access, or other malicious actions within containerized environments, ensuring the integrity and security of applications running in containers. The system is particularly useful in cloud-native and microservices architectures where containers are widely deployed.
21. The system of claim 20 , wherein the learned behavior includes at least one of: hypertext transfer protocol (HTTP) verbs, application programming interface (API) routes, and query parameters.
This invention relates to a system for analyzing and modeling learned behavior in networked applications, particularly focusing on web-based interactions. The system is designed to address challenges in understanding and predicting how applications interact with web services, APIs, and data endpoints. It captures and processes behavioral patterns from network traffic, including HTTP requests and responses, to identify and classify recurring interactions. The system includes a behavior learning module that extracts and analyzes specific elements of web traffic, such as HTTP verbs (e.g., GET, POST, PUT, DELETE), API routes (e.g., endpoints like /api/users), and query parameters (e.g., ?id=123). These elements are used to construct a model of expected behavior, which can then be applied to detect anomalies, enforce security policies, or optimize performance. The system may also include a monitoring component that compares real-time traffic against the learned behavior model to identify deviations or unauthorized actions. By focusing on these key aspects of web interactions, the system provides a structured way to understand and control how applications communicate over networks, improving security, debugging, and automation in web-based environments. The invention is particularly useful in scenarios where applications must adhere to strict API contracts or where unauthorized modifications to expected behavior could lead to vulnerabilities or errors.
22. The system of claim 20 , wherein the learned behavior includes a pattern of requests and corresponding responses.
A system for analyzing and modeling user behavior in a networked environment, particularly for identifying and predicting patterns of requests and corresponding responses. The system captures interactions between users and a networked service, such as a server or application, to detect recurring sequences of requests and the system's responses. By learning these patterns, the system can anticipate future requests and optimize performance, security, or resource allocation. The learned behavior may include temporal aspects, such as the timing or frequency of requests, and contextual factors, such as the conditions under which specific responses are generated. This enables the system to adapt dynamically to user behavior, improving efficiency and reducing latency. The system may also apply machine learning techniques to refine its models over time, enhancing accuracy in predicting and responding to user interactions. The learned patterns can be used for various purposes, including load balancing, anomaly detection, or personalized service delivery. The system operates in real-time or near-real-time, continuously updating its models to reflect evolving user behavior. This approach is particularly useful in environments where user interactions are complex or variable, such as cloud computing, web applications, or IoT networks.
23. The system of claim 20 , wherein the learned behavior includes user interactions with the software container.
A system for analyzing and adapting to user behavior within a software environment. The system monitors and records user interactions with a software container, which is a self-contained execution environment for applications. The software container may include virtual machines, application containers, or other isolated runtime environments. The system captures user actions such as input commands, navigation patterns, and configuration changes within the container. It then processes this data to identify behavioral patterns, preferences, and usage trends. The learned behavior is used to optimize the software container's performance, security, or user experience. For example, the system may preload frequently accessed resources, adjust security policies based on detected threats, or suggest workflow improvements. The system may also compare user behavior across multiple containers to identify anomalies or inefficiencies. The goal is to enhance productivity, reduce errors, and improve the overall usability of the software container. The system may integrate with existing container management platforms or operate as a standalone analytics tool.
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February 18, 2020
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